Entity relation extraction mainly extracts relations from text, which is one of the important tasks of natural language processing. At present, some special fields have insufficient data; for example, agriculture, the metallurgical industry, etc. There is a lack of an effective model for entity relationship recognition under the condition of insufficient data. Inspired by this, we constructed a suitable small balanced data set and proposed a multi-neural network collaborative model (RBF, Roberta–Bidirectional Gated Recurrent Unit–Fully Connected). In addition, we also optimized the proposed model. This model uses the Roberta model as the coding layer, which is used to extract the word-level features of the text. This model uses BiGRU (Bidirectional Gated Recurrent Unit)–FC (Fully Connected) as the decoding layer, which is used to obtain the optimal relationship of the text. To further improve the effect, the input layer is optimized by feature fusion, and the learning rate is optimized by the cosine annealing algorithm. The experimental results show that, using the small balanced data set, the F1 value of the RBF model proposed in the paper is 25.9% higher than the traditional Word2vec–BiGRU–FC model. It is 18.6% higher than the recent Bert–BiLSTM (Bidirectional Long Short Term Memory)–FC model. The experimental results show that our model is effective.

With the expanding enrollments in higher education, the quality of college education and the learning gains of students have attracted much attention. It is important to study the influencing factors and mechanisms of individual students’ acquisition of learning gains to improve the quality of talent cultivation in colleges. However, in the context of information security, the original data of learning situation surveys in various universities involve the security of educational evaluation data and daily privacy of teachers and students. To protect the original data, data feature mining and correlation analyses were performed at the model level. This study selected 12,181 pieces of data from X University, which participated in the Chinese College Student Survey (CCSS) from 2018 to 2021. A confirmatory factor analysis was conducted and a structural equation modeling was conducted using AMOS 24.0. Through hypothesis testing, this study explored the mechanisms that influence learning gains from the perspectives of student involvement, teacher involvement, and school support. The results indicated that the quality of student involvement has an important mediating effect on learning gains and that a supportive campus environment has the greatest influence on learning gains. Establishing positive emotional communications between teachers and students is a more direct and effective method than improving the teaching level to improve the quality of student involvement. This study discusses the implications of these results on the research and practice of connotative development in higher education.

Knowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the traditional expert system of question and answer, it has the advantage of a large-scale background knowledge base and the traceability and interpretability of the question-answering process. Compared with the current ChatGPT (Chat Generative Pre-trained Transformer) technology, it has advantages in the proprietary segmentation field. Aiming at the problem of the accuracy of existing knowledge question-answering methods being low, this paper studies the method of semantic analysis for knowledge question-answering under the support of a knowledge database, proposes a knowledge question-answering method based on the superposition of multiple neural network models, and conducts experimental verification on the publicly available NLPCC2016KBQA(Knowledge Q&A Tasks in the 2016 Natural Language Processing and Chinese Computing Conference) data set. The experimental results show that the F1 value of this method is higher than that of the baseline model.

Cyberspace has evolved into a hub for the dissemination of large amounts of tendentious information, posing significant challenges to the role of mainstream value information. As netizens’ are the main recipients of tendentious information, their personal cognition, attitude, and behavioral ability affect their willingness to re-disseminate information, making them an inalienable force in the promotion of information dissemination. Exploring the dissemination mechanism of tendentious information in cyberspace can help to understand the law of information dissemination and predict the trend of information diffusion, which is of great significance to maintaining information security and social stability. However, the existing research has overlooked the potential influence of the attribute characteristics of information in terms of content, and has failed to overcome the methodological constraints of traditional statistical analysis to accurately describe the variables and mechanisms influencing the dissemination of tendentious information at the cognitive level. Therefore, using structural equation modeling, we propose a research index system based on the Theory of Planned Behavior and the characteristics of tendentious information. To this end, confirmatory factor and model fitting analyses were conducted to develop a tendentious information dissemination mechanism model, which we validated through testing and comparative experiments. Path analysis revealed that Attitude Toward Dissemination, Information Dissemination Intention, and Information Dissemination Behavior are the main links in the information dissemination process. Moreover, Information Sentiment Orientation was found to strongly promote the dissemination of tendentious information, while Subject Norm of Dissemination had a minor inhibiting effect.